IEEE 802.11p protocol, which is also called Wireless Access in Vehicular Environment (WAVE), is an approved amendment to the IEEE 802.11. It is an extension of IEEE 802.11 and conforms to corresponding applications in Intelligent Transportation System (ITS).
Similar with other 802.11 family members, frame synchronization of 802.11p mainly depends on short and long training sequences. An example of such training sequences is illustrated in FIG. 1. In this example, there are ten short training sequences t1˜t10 and two long training sequences T1˜T2, and the total training time is 32 μs.
In some conventional methods, frame synchronization is split into a coarse timing process and a fine timing process. In the coarse timing process, the position of a data frame is approximately located using a first pre-set threshold and a first timing metric function based on self-correlation of the received signal. In the fine timing process, the position of the data frame transmitted in the first path is located using a second pre-set threshold and a second timing metric function based on cross-correlation between the received signal and the known training sequence. A data frame means a data packet containing certain training sequences and payload.
In such methods, the second timing metric function based on cross-correlation is used only when a data frame is detected. Since computation based on self-correlation has low complexity and relatively low precision, and computation based on cross-correlation has relatively high complexity and high precision, relatively high precision at relatively low computation complexity can be achieved by such combination.
The S&C algorithm is taken as an example to illustrate a conventional coarse timing method.
A timing metric function may be defined as Equation (1),
                                          M            1                    ⁡                      (            d            )                          =                                                                          P                ⁡                                  (                  d                  )                                                                    2                                              R              2                        ⁡                          (              d              )                                                          Equation        ⁢                                  ⁢                  (          1          )                    
where d represents position along time axis.
P(d) may be defined as Equation (2),
                              P          ⁡                      (            d            )                          =                              ∑                          k              =              0                                                      N                /                2                            -              1                                ⁢                                    r              ⁡                              (                                  d                  +                                      N                    /                    2                                    +                  k                                )                                      ⁢                          r              ⁡                              (                                  d                  +                  k                                )                                                                        Equation        ⁢                                  ⁢                  (          2          )                    
where r(n) represents the received signal, and N represents the length of a short training sequence.
R(d) may be defined as Equation (3).
                              R          ⁡                      (            d            )                          =                              ∑                          k              =              0                                                      N                /                2                            -              1                                ⁢                                          ⁢                                                                  r                ⁡                                  (                                      d                    +                                          N                      /                      2                                        +                    k                                    )                                                                    2                                              Equation        ⁢                                  ⁢                  (          3          )                    
A threshold Cth may be pre-set. If there are a predetermined number of consecutive timing metric values greater than Cth, it indicates that there is a data frame, and coarse timing ends. If M1(d)≦Cth, it indicates that there is no data frame, and coarse timing continues. An example of how a data frame is found is shown in FIG. 2.
However, related parameters of a signal channel may change such as due to different kinds of fading. As a result, a pre-set threshold may cause errors in certain cases. For example, in some cases, the pre-set threshold may be too low, and noise may be identified as a data frame by mistake. In some cases, the pre-set threshold may be too high, and the beginning part of a frame may be missed. Therefore, more robust coarse timing methods and systems are needed.